Predicting Internet Attention

Description

In aggregate, billions of individuals click, read, watch, and share hundreds of millions of pieces of unique content every day. A widely-used content/audience analytics platform has a unique dataset in this area. In this talk we will ask and answer this question: can studying Internet traffic today help to predict web-wide attention -- and thus real-world events -- tomorrow?

Abstract:

In aggregate, billions of individuals click, read, watch, and share hundreds of millions of pieces of unique content every day. In this talk we will ask and answer this question: can studying Internet traffic today help to predict web-wide attention -- and thus real-world events -- tomorrow?

By studying a proprietary data set built up over years by Parse.ly, a widely-used content analytics platform, we can learn much about how information flows online. This includes 240 billion user events collected from around 1 billion individuals over a one-year period in 2016 -- aggregated across over 1,000 popular web sites. The observations and predictions available in this data have possible implications for digital marketing, public relations, government policy, and financial research. The size and shape of the data also provide fertile ground for applying machine learning techniques in the areas of classification/clustering and predictive analytics.